DeepCSFusion: Deep Compressive Sensing Fusion for Efficient COVID-19 Classification.

Worldwide, the COVID-19 epidemic, which started in 2019, has resulted in millions of deaths. The medical research community has widely used computer analysis of medical data during the pandemic, specifically deep learning models. Deploying models on devices with constrained resources is a significant challenge due to the increased storage demands associated with larger deep learning models. Accordingly, in this paper, we propose a novel compression strategy that compresses deep features with a compression ratio of 10 to 90% to accurately classify the COVID-19 and non-COVID-19 computed tomography scans. Additionally, we extensively validated the compression using various available deep learning methods to extract the most suitable features from different models. Finally, the suggested DeepCSFusion model compresses the extracted features and applies fusion to achieve the highest classification accuracy with fewer features. The proposed DeepCSFusion model was validated on the publicly available dataset "SARS-CoV-2 CT" scans composed of 1252 CT. This study demonstrates that the proposed DeepCSFusion reduced the computational time with an overall accuracy of 99.3%. Also, it outperforms state-of-the-art pipelines in terms of various classification measures.

Evaluation of CTLA-4 (+ 49A/G) polymorphism association with rheumatoid arthritis in Egyptian patients.

Cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) is an inhibitory molecule that has an essential role in T-cell homeostasis and self-tolerance because of its inhibitory signals. Genetic polymorphisms in the CTLA-4 gene have been associated with several autoimmune diseases. We aimed to assess the association between the CTLA-4 +49 A/G polymorphism (rs231775) and rheumatoid arthritis (RA) in Egyptian RA patients. The study included 104 RA patients and 81 apparently healthy control individuals. The polymorphism was assessed using restriction fragment-length polymorphism analysis. Genotype distribution was compared between patients and controls under different models of inheritance. Under the codominant model, RA patients showed a higher frequency of AG and GG genotypes compared to the control subjects (p=0.0092). Under the dominant model, RA patients showed a higher frequency of AG and GG genotypes grouped together compared to control subjects (p=0.0026). Under the over-dominant model, the AG genotype was more frequent in RA patients compared to control subjects (p= 0.0395). No association was observed between CTLA-4 polymorphism rs231775 and RA using the recessive model (p=0.1356). A significant association was observed between carrying the G allele and the presence of RA (p=0.0032). In conclusion, our findings showed a positive association between the CTLA-4 gene +49 A>G polymorphism and RA. However, discrepancies in literature reflect both ethnic variability in CTLA-4 gene polymorphisms as well as the complex pathogenesis of RA.

In vitro expansion of human mesenchymal stem cells and hepatocytes using purified platelet derived growth factors, a potential therapeutic modality for liver fibrosis.

Mesenchymal stem cells (MSCs) and hepatocytes are considered valuable candidates for cell-based therapy. The use of free zoonotic media, as purified platelets derived growth factors (L-GF) and human platelet lysate (hPL), instead of using fetal bovine serum (FBS) to support the growth and proliferation of these cells could be used as a promising therapeutic tool in hepatic regeneration. This study aimed to evaluate the usage of purified platelet derived growth factors and platelet lysate in both MSCs and hepatocyte cultures and to compare them with the usage of FBS. MSCs and hepatocytes were cultured in growth media supplemented with L-GF or hPL and compared them to their culture in growth media supplemented with FBS. Cells were subjected to population doubling (PD) and generation time (GT) calculations. The best result for MSCs was that obtained by using 10% hPL or 10% FBS with the highest cell count, highest viability and shortest incubation time needed to reach confluency compared to supplementation with 10%, 20% or 30% L-GF. As for hepatocyte culture, the use of 10% FBS for supplementation of media used for hepatic cell proliferation showed better performance regarding cell count, viability, and incubation time to reach confluency compared to the use of either hPL or L-GF. In conclusion, our study showed that 10% hPL had the best results in MSCs culture which suggests that hPL could be a better alternative for the development of xenofree stem cell culture that can be used for many clinical applications. On the other hand, 10% FBS showed the best results in hepatocyte culture.

Krebs von den Lungen-6, a promising marker in evaluating the severity of interstitial lung disease in Egyptian rheumatoid arthritis patients.

Krebs von den Lungen-6 (KL-6) is one of the mucins associated with interstitial lung disease. We aimed to assess the value of KL-6 as a marker for detecting the presence of interstitial lung disease in Egyptian rheumatoid arthritis patients and to evaluate its ability to assess severity in different grades of interstitial lung disease. The study included 89 rheumatoid arthritis patients; 64 patients with interstitial lung disease and 25 patients without interstitial lung disease. Serum levels of KL-6 were assessed using enzyme linked immunosorbent assay. Levels of KL-6 were higher in patients with interstitial lung disease compared to patients without interstitial lung disease (P< 0.001). KL-6 levels were significantly higher in grade 4 patients than those in grades 1 and 2. Also, KL-6 levels were significantly higher in grade 3 patients than those in grades 1 and 2. Kl-6 levels were also higher in grade 2 patients compared to grade 1 patients. Finally, no difference was observed between grade 4 patients and grade 3 patients. KL-6 levels were significantly higher in usual interstitial pneumonia pattern compared other patterns (P=0.015). In conclusion, KL-6 is a potential circulating biomarker that may have a substantial role in detecting the presence and evaluating the severity of interstitial lung disease among rheumatoid arthritis patients.

A framework for breast cancer classification using Multi-DCNNs.

BACKGROUND: Deep learning (DL) is the fastest-growing field of machine learning (ML). Deep convolutional neural networks (DCNN) are currently the main tool used for image analysis and classification purposes. There are several DCNN architectures among them AlexNet, GoogleNet, and residual networks (ResNet). METHOD: This paper presents a new computer-aided diagnosis (CAD) system based on feature extraction and classification using DL techniques to help radiologists to classify breast cancer lesions in mammograms. This is performed by four different experiments to determine the optimum approach. The first one consists of end-to-end pre-trained fine-tuned DCNN networks. In the second one, the deep features of the DCNNs are extracted and fed to a support vector machine (SVM) classifier with different kernel functions. The third experiment performs deep features fusion to demonstrate that combining deep features will enhance the accuracy of the SVM classifiers. Finally, in the fourth experiment, principal component analysis (PCA) is introduced to reduce the large feature vector produced in feature fusion and to decrease the computational cost. The experiments are performed on two datasets (1) the curated breast imaging subset of the digital database for screening mammography (CBIS-DDSM) and (2) the mammographic image analysis society digital mammogram database (MIAS). RESULTS: The accuracy achieved using deep features fusion for both datasets proved to be the highest compared to the state-of-the-art CAD systems. Conversely, when applying the PCA on the feature fusion sets, the accuracy did not improve; however, the computational cost decreased as the execution time decreased.

Monocyte monolayer assay in pre-transfusion testing: A magic key in transfusing patients with recurrent bad cross-match due to alloimmunization.

BACKGROUND: The monocyte monolayer assay (MMA) is an in-vitro assay that can predict the outcome of blood transfusion of antigen positive units when serologically compatible blood is not available. MATERIALS AND METHODS: Fifty-four patients testing positive by the antibody screening test using gel agglutination were further examined by the alloantibody identification panel to determine alloantibody specificity. After determining and categorizing the antibodies, patients' samples were examined using the MMA to determine the clinical significance of the detected alloantibodies. We also tested 2 seeding methods (24-well cell culture plates versus 8-well chamber-slides) and 3 visualization/staining techniques (unstained phase contrast, Leishman and Giemsa staining). RESULTS: 35 out of the 54 cases (64.8%) had a monocyte index of >5% which is predictive of occurrence of hemolytic reaction after transfusion; 23 cases with antibodies known to be clinically significant [anti-C, anti-E, anti-c, anti-K, anti-Fy(a), anti Fy(b), anti-JK(b)], 2 with Anti-M specificity, 7 cases with autoantibodies and 3 cases with multiple antibodies. On the other hand, 19 out of the 54 (35.2%) cases included in the study showed a monocyte index of <5% which is predictive of absence of hemolytic reaction after transfusion. The 8-well chamber-slides were better than the 24-well culture plates, as the latter showed a lot of un-phagocytosed RBCs in the background. Also, Leishman staining was better than Giemsa staining with better and clearer differentiation between the RBCs, monocytes and phagocytic vacuoles. CONCLUSION: MMA can be used as a surrogate cross-match test for the selection of blood units in cases where antigen-negative blood units are not available.

Chronic hepatitis C virus infection impairs natural killer cells-dendritic cells cross-talk: An in vitro culture study.

To examine the cross-talk between NK cells and DCs in hepatitis C virus (HCV) infection, we isolated monocytes and NK cells from 20 chronic HCV patients and 20 healthy controls. Monocytes were used to generate immature DCs which were pulsed with HCV peptides (core, NS3-NS4, and NS5). Four different cocultures were carried out: E1, both DCs and NK cells were from a chronic HCV patient; E2, NK cells from a healthy control cocultured with DCs from a chronic HCV patient; E3, NK cells from a chronic HCV patient cocultured with DCs from a healthy control; and E4, both DCs and NK cells were from a healthy control. Using flow cytometry, we assessed the effect of these different cocultures on levels of maturation markers on DCs and levels of activation/inhibition markers on NK cells. Results showed that peptide-pulsed HCV DCs showed a maturation defect in the form of decreased HLA-DR, decreased CD86, and increased CD83 expression especially when cocultured with HCV NK. This was mainly due to core peptide pulsing and to a lesser extent due to NS5 pulsing, whereas there was no effect with NS3-NS4 pulsing. Alternatively, HCV NK cells upregulated both activation and inhibition markers especially when cocultured with healthy DCs. Compared with E2, E1 resulted in higher apoptosis of both NK cells and DCs with the percentage of NK apoptosis higher than that of DCs. Taken together, the data indicate that HCV infection impairs NK-DC cross-talk which may be a leading cause in viral persistence and chronicity.

MULTI-DEEP: A novel CAD system for coronavirus (COVID-19) diagnosis from CT images using multiple convolution neural networks.

Coronavirus (COVID-19) was first observed in Wuhan, China, and quickly propagated worldwide. It is considered the supreme crisis of the present era and one of the most crucial hazards threatening worldwide health. Therefore, the early detection of COVID-19 is essential. The common way to detect COVID-19 is the reverse transcription-polymerase chain reaction (RT-PCR) test, although it has several drawbacks. Computed tomography (CT) scans can enable the early detection of suspected patients, however, the overlap between patterns of COVID-19 and other types of pneumonia makes it difficult for radiologists to diagnose COVID-19 accurately. On the other hand, deep learning (DL) techniques and especially the convolutional neural network (CNN) can classify COVID-19 and non-COVID-19 cases. In addition, DL techniques that use CT images can deliver an accurate diagnosis faster than the RT-PCR test, which consequently saves time for disease control and provides an efficient computer-aided diagnosis (CAD) system. The shortage of publicly available datasets of CT images, makes the CAD system's design a challenging task. The CAD systems in the literature are based on either individual CNN or two-fused CNNs; one used for segmentation and the other for classification and diagnosis. In this article, a novel CAD system is proposed for diagnosing COVID-19 based on the fusion of multiple CNNs. First, an end-to-end classification is performed. Afterward, the deep features are extracted from each network individually and classified using a support vector machine (SVM) classifier. Next, principal component analysis is applied to each deep feature set, extracted from each network. Such feature sets are then used to train an SVM classifier individually. Afterward, a selected number of principal components from each deep feature set are fused and compared with the fusion of the deep features extracted from each CNN. The results show that the proposed system is effective and capable of detecting COVID-19 and distinguishing it from non-COVID-19 cases with an accuracy of 94.7%, AUC of 0.98 (98%), sensitivity 95.6%, and specificity of 93.7%. Moreover, the results show that the system is efficient, as fusing a selected number of principal components has reduced the computational cost of the final model by almost 32%.

The COVID-19 Cytokine Storm; What We Know So Far.

COVID-19 is a rapidly spreading global threat that has been declared as a pandemic by the WHO. COVID-19 is transmitted via droplets or direct contact and infects the respiratory tract resulting in pneumonia in most of the cases and acute respiratory distress syndrome (ARDS) in about 15 % of the cases. Mortality in COVID-19 patients has been linked to the presence of the so-called "cytokine storm" induced by the virus. Excessive production of proinflammatory cytokines leads to ARDS aggravation and widespread tissue damage resulting in multi-organ failure and death. Targeting cytokines during the management of COVID-19 patients could improve survival rates and reduce mortality.

FUSI-CAD: Coronavirus (COVID-19) diagnosis based on the fusion of CNNs and handcrafted features.

The precise and rapid diagnosis of coronavirus (COVID-19) at the very primary stage helps doctors to manage patients in high workload conditions. In addition, it prevents the spread of this pandemic virus. Computer-aided diagnosis (CAD) based on artificial intelligence (AI) techniques can be used to distinguish between COVID-19 and non-COVID-19 from the computed tomography (CT) imaging. Furthermore, the CAD systems are capable of delivering an accurate faster COVID-19 diagnosis, which consequently saves time for the disease control and provides an efficient diagnosis compared to laboratory tests. In this study, a novel CAD system called FUSI-CAD based on AI techniques is proposed. Almost all the methods in the literature are based on individual convolutional neural networks (CNN). Consequently, the FUSI-CAD system is based on the fusion of multiple different CNN architectures with three handcrafted features including statistical features and textural analysis features such as discrete wavelet transform (DWT), and the grey level co-occurrence matrix (GLCM) which were not previously utilized in coronavirus diagnosis. The SARS-CoV-2 CT-scan dataset is used to test the performance of the proposed FUSI-CAD. The results show that the proposed system could accurately differentiate between COVID-19 and non-COVID-19 images, as the accuracy achieved is 99%. Additionally, the system proved to be reliable as well. This is because the sensitivity, specificity, and precision attained to 99%. In addition, the diagnostics odds ratio (DOR) is ≥ 100. Furthermore, the results are compared with recent related studies based on the same dataset. The comparison verifies the competence of the proposed FUSI-CAD over the other related CAD systems. Thus, the novel FUSI-CAD system can be employed in real diagnostic scenarios for achieving accurate testing for COVID-19 and avoiding human misdiagnosis that might exist due to human fatigue. It can also reduce the time and exertion made by the radiologists during the examination process.

Longer duration of kangaroo care improves neurobehavioral performance and feeding in preterm infants: a randomized controlled trial.

AIM: To investigate the effect of kangaroo care (KC) and its duration on neurobehavioral performance, stress response, breastfeeding success, and vital signs in premature infants. METHODS: One hundred and twenty premature infants were randomized to receive either KC for 60 min daily, KC for 120 min daily or conventional care (controls) for at least 7 days. Salivary cortisol was measured before and after the first KC session and then after 7 days. Temperature, respiration rate, heart rate, and oxygen saturation were recorded, before and after KC. Neonates were evaluated by the Neonatal Intensive Care Unit Network Neurobehavioral Scale (NNNS). RESULTS: Both KC groups demonstrated higher scores for attention, arousal, regulation, nonoptimal reflexes, and quality of movements and lower scores for handling, excitability, and lethargy, compared to controls (p < 0.05). Both KC groups had higher infant breastfeeding assessment tool score and reached full enteral feeds faster than controls (p < 0.05). After the first KC session, improvement in O2 saturation and temperature was observed in KC 120-min group compared with the KC 60-min group (p < 0.05). Salivary cortisol decreased in both KC groups compared with controls after 7 days (p < 0.05). CONCLUSION: Preterm neonates who receive KC for long durations reach full enteral feeds faster, have better breastfeeding success, neurobehavioral performance, thermal control, and tissue oxygenation.

Breast Cancer Diagnosis Using an Efficient CAD System Based on Multiple Classifiers.

Breast cancer is one of the major health issues across the world. In this study, a new computer-aided detection (CAD) system is introduced. First, the mammogram images were enhanced to increase the contrast. Second, the pectoral muscle was eliminated and the breast was suppressed from the mammogram. Afterward, some statistical features were extracted. Next, k-nearest neighbor (k-NN) and decision trees classifiers were used to classify the normal and abnormal lesions. Moreover, multiple classifier systems (MCS) was constructed as it usually improves the classification results. The MCS has two structures, cascaded and parallel structures. Finally, two wrapper feature selection (FS) approaches were applied to identify those features, which influence classification accuracy. The two data sets (1) the mammographic image analysis society digital mammogram database (MIAS) and (2) the digital mammography dream challenge were combined together to test the CAD system proposed. The highest accuracy achieved with the proposed CAD system before FS was 99.7% using the Adaboosting of the J48 decision tree classifiers. The highest accuracy after FS was 100%, which was achieved with k-NN classifier. Moreover, the area under the curve (AUC) of the receiver operating characteristic (ROC) curve was equal to 1.0. The results showed that the proposed CAD system was able to accurately classify normal and abnormal lesions in mammogram samples.

Breast cancer detection using deep convolutional neural networks and support vector machines.

It is important to detect breast cancer as early as possible. In this manuscript, a new methodology for classifying breast cancer using deep learning and some segmentation techniques are introduced. A new computer aided detection (CAD) system is proposed for classifying benign and malignant mass tumors in breast mammography images. In this CAD system, two segmentation approaches are used. The first approach involves determining the region of interest (ROI) manually, while the second approach uses the technique of threshold and region based. The deep convolutional neural network (DCNN) is used for feature extraction. A well-known DCNN architecture named AlexNet is used and is fine-tuned to classify two classes instead of 1,000 classes. The last fully connected (fc) layer is connected to the support vector machine (SVM) classifier to obtain better accuracy. The results are obtained using the following publicly available datasets (1) the digital database for screening mammography (DDSM); and (2) the Curated Breast Imaging Subset of DDSM (CBIS-DDSM). Training on a large number of data gives high accuracy rate. Nevertheless, the biomedical datasets contain a relatively small number of samples due to limited patient volume. Accordingly, data augmentation is a method for increasing the size of the input data by generating new data from the original input data. There are many forms for the data augmentation; the one used here is the rotation. The accuracy of the new-trained DCNN architecture is 71.01% when cropping the ROI manually from the mammogram. The highest area under the curve (AUC) achieved was 0.88 (88%) for the samples obtained from both segmentation techniques. Moreover, when using the samples obtained from the CBIS-DDSM, the accuracy of the DCNN is increased to 73.6%. Consequently, the SVM accuracy becomes 87.2% with an AUC equaling to 0.94 (94%). This is the highest AUC value compared to previous work using the same conditions.

Elevated levels of IL-37 correlate with T cell activation status in rheumatoid arthritis patients.

OBJECTIVE: To assess the association between serum levels of IL-37 in rheumatoid arthritis patients and percentage of peripheral blood T lymphocytes expressing the activation marker CD26 and investigate their correlation with disease activity. METHODS: The study included 48 rheumatoid arthritis patients and 42 age and sex matched healthy controls. Serum levels of IL-37 were determined using enzyme linked immunosorbent assay while percentage of CD3+CD26+T cells in peripheral blood mononuclear cells was assayed using flowcytometry. RESULTS: Serum levels of IL-37, as well as the percentage of CD3+CD26+T cells, were significantly higher in rheumatoid arthritis patients than in healthy controls. Also, serum IL-37 levels were higher in patients with severe disease activity than patients with moderate and low disease activity. In rheumatoid arthritis patients, both serum levels of IL-37 and percentage of CD3+CD26+T cells correlated with disease activity (DAS28), C-reactive protein levels and erythrocyte sedimentation rate. In addition, serum levels of the anti-inflammatory cytokine IL-37 positively correlated with the percentage of CD3+CD26+T cells in peripheral blood of rheumatoid arthritis patients. CONCLUSION: Our results indicate a strong correlation between serum levels of IL-37 and frequency of activated T cells in peripheral blood of rheumatoid arthritis patients. Our results suggest that in an active disease status, activated T lymphocytes may be a contributing source to the elevated levels of IL-37 trying to down-regulate the active inflammatory process.

Transient elastography as a noninvasive assessment tool for hepatopathies of different etiology in pediatric type 1 diabetes mellitus.

AIM: To identify the prevalence and effect of hepatopathies of different etiologies among pediatric patients with type 1 diabetes mellitus (T1DM) using transient elastography (TE) and its relation to glycemic control. METHODS: One hundred T1DM patients were studied focusing on liver functions, fasting lipid profile, hemoglobin A1c (HbA1c), hepatitis C virus (HCV), serum immunoglobulins, autoimmune antibodies; anti-nuclear antibody (ANA), anti-smooth muscle antibody (ASMA), and anti-liver kidney microsomal antibody (anti-LKM). Abdominal ultrasound was performed and TE was done for patients with HCV, positive autoimmune antibody and/or abnormal ultrasound findings. RESULTS: Thirty-one patients were found to have one or more hepatic abnormalities; clinical hepatomegaly in 8%, elevated alanine aminotransferase (ALT) in 10%, HCV in 6%, autoimmune hepatitis (AIH) in 11% (10 were positive for ASMA and 2 were positive for ANA while anti-LKM antibodies were negative) and abnormal hepatic ultrasound in 20% (12 non-alcoholic fatty liver disease, 5 AIH, 2 HCV, 1 Mauriac syndrome). Mean liver stiffness in those 31 patients was 7.0±2.1kPa (range, 3.1-11.8kPa); 24 were Metavir F0-F1, 7 were F2-F3 while none was F4. Type 1 diabetic patients with abnormal hepatic ultrasound had higher fasting blood glucose, HbA1c and total cholesterol than those with normal findings. Liver stiffness was significantly higher in patients with abnormal liver ultrasound compared with normal sonography. Liver stiffness was positively correlated to HbA1c and ALT. CONCLUSIONS: Hepatic abnormalities are prevalent in T1DM and related to poor metabolic control. TE provides a non-invasive method for detection of hepatopathy-induced fibrosis.

Characterization of differential antibody production against hepatitis C virus in different HCV infection status.

BACKGROUND: The Centers for Disease Control and Prevention (CDC) issued an update on hepatitis C virus (HCV) testing approach, in which it omitted the use of recombinant immunoblot assay (RIBA) in the diagnostic algorithm and recommended that future studies are needed to evaluate the performance of HCV testing without RIBA. As Egypt has the highest prevalence of HCV worldwide, we aimed to evaluate the value of RIBA in HCV testing in a high prevalence population. Our objective was to clarify whether enzyme linked immunosorbent assay (ELISA) anti-HCV signal-to-cutoff (S/CO) ratios were able to discriminate true positive from false positive anti-HCV antibody status and to evaluate the role of RIBA in solving this problem which may lead to a redefined strategy for diagnosis of HCV infection. Our second objective was to elucidate the effects of different HCV peptides of both structural and non-structural proteins on the humoral immune response to HCV infection. METHODS: The current study drew results from 167 individuals divided into three groups: Group I: included 77 HCV antibody positive (ELISA) high risk health care workers (HCW), Group II: included 56 presumably uninfected individuals who showed normal liver enzymes, negative HCV RNA and were asymptomatic. Their ELISA HCV antibody S/C ratio ranged from 0.9 to <5. Group III: included 34 patients enrolled from outpatient clinics of Ain Shams Hospital with persistent viral replication, elevated liver enzymes, and chronic HCV related liver disease. All study participants were assessed for the presence of anti-HCV antibodies by 3(rd) generation ELISA which was confirmed by RIBA. RESULTS: Interpreting the results of both ELISA and RIBA together, false positive results were highly significantly increased in HCW when compared with the other two groups. Indeterminate and false negative results were only found in the presumably uninfected group. For differentiated antibody responses by RIBA, chronic HCV cases had the highest frequency of positive antibody response to core peptides while the presumably uninfected group had the lowest. Antibody response to E2 was found less frequently in chronic cases than Core 1, Core 2 and NS3. The specific antibody response to the different HCV peptides showed the same distribution of frequencies in both chronic HCV cases and the presumably uninfected individuals with the chronic cases having the highest frequencies. This distribution was different from the HCW. The most evident difference was the reaction towards NS3 which was the highest antibody producing peptide in chronic HCV and presumably uninfected individuals whereas in HCW Core1 was the highest. CONCLUSION: The HCV antibody immunoblot assay (RIBA) is still necessary for the detection of false positive cases which can occur quite frequently in countries of high prevalence as Egypt. Indeterminate RIBA results indicate a waning antibody response in elderly individuals who recovered from previous or distant HCV infection.

Vitamin D status and its modulatory effect on interferon gamma and interleukin-10 production by peripheral blood mononuclear cells in culture.

OBJECTIVES: We aimed to investigate the influence of vitamin D on the production of the pro-inflammatory cytokine, interferon gamma (IFN-γ), and the anti-inflammatory cytokine, interleukin-10 (IL-10), in peripheral blood mononuclear cell (PBMC) cultures. METHODS: Thirty healthy subjects were investigated. Serum levels of calcium, 25(OH) D, and parathyroid hormone (PTH) were assessed. PBMCs were activated in-vitro by phytohemagglutinin (PHA) in the presence and absence of vitamin D3 and then levels of IFN-γ and IL-10 were determined in culture supernatant using enzyme immunoassay. RESULTS: Serum calcium levels were significantly lower in the vitamin D deficient group while serum PTH levels were significantly higher in the vitamin D deficient group. PTH levels were inversely correlated to both calcium and 25(OH) D levels. In culture, vitamin D inhibited IFN-γ production and increased IL-10 production by PBMCs. Serum vitamin D status had no influence on the amount of cytokine produced in culture. CONCLUSIONS: This study demonstrates that vitamin D modulates IFN-γ and IL-10 production and provides a rationale for evaluating vitamin D as an immunomodulatory agent.

Sexual Transmission of HCV in Heterologous Monogamous Spouses.

We screened for evidence of HCV infection in healthy heterologous monogamous spouses of chronic HCV patients and studied the relation with various risk factors. A cross-sectional study of fifty healthy monogamous heterosexual spouses of HCV-positive index cases was carried out. All participants were HBV and HIV negative. The association with various risk factors was studied. Five spouses (10%) showed evidence of HCV infection. Two partners were positive for HCV antibody alone (4%) and 3 for antibody and HCV PCR (6%). No association was found between HCV infection and various sociodemographic parameters with the exception of older age categories. Intraspousal transmission of HCV may be an important source of spread of HCV infection. The reservoir of HCV-infected individuals in Egypt is sizable, and sexual transmission of HCV may contribute to the total burden of infection in Egypt.

CXCL10 antagonism and plasma sDPPIV correlate with increasing liver disease in chronic HCV genotype 4 infected patients.

Egypt has the highest prevalence of hepatitis C virus infection worldwide. CXCL10 is a potent chemoattractant that directs effector lymphocytes to sites of inflammation. It has been reported that plasma CXCL10 is processed by dipeptidylpeptidase IV (DPPIV) thus leading to the generation of an antagonist form. Using Luminex-based immunoassays we determined the concentration of different forms of CXCL10 (total, agonist, and antagonist). We also evaluated plasma soluble DPPIV (sDPPIV) concentration and plasma dipeptidylpeptidase (DPP) activity. Using flow cytometry and immunohistochemistry, we analyzed the distribution of lymphocyte subsets. Plasma CXCL10 was elevated in chronic HCV patients, however the agonist form was undetectable. Increased sDPPIV concentration and DPP activity supported the NH2-truncation of CXCL10. Finally, we demonstrated an increased frequency of CXCR3(+) cells in the peripheral blood, and low numbers of CXCR3(+) cells within the lobular regions of the liver. These findings generalize the observation of chemokine antagonism as a mechanism of immune modulation in chronic HCV patients and may help guide the use of new therapeutic immune modulators.